


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__	):
					lowercase_					=      StableDiffusionPipeline.from_pretrained(a__    ,				torch_dtype=torch.floataa	)

					# load LoRA weight from .safetensors
					lowercase_					=      load_file(a__	)

					lowercase_					=      []

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															lowercase_					=      key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
															lowercase_					=      pipeline.text_encoder
										else:
															lowercase_					=      key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
															lowercase_					=      pipeline.unet

										# find the target layer
										lowercase_					=      layer_infos.pop(0	)
										while len(a__	) > -1:
															try:
																				lowercase_					=      curr_layer.__getattr__(a__	)
																				if len(a__	) > 0:
																									lowercase_					=      layer_infos.pop(0	)
																				elif len(a__	) == 0:
																									break
															except Exception:
																				if len(a__	) > 0:
																									temp_name += "_" + layer_infos.pop(0	)
																				else:
																									lowercase_					=      layer_infos.pop(0	)

										lowercase_					=      []
										if "lora_down" in key:
															pair_keys.append(key.replace("""lora_down"""    ,				"""lora_up"""	)	)
															pair_keys.append(a__	)
										else:
															pair_keys.append(a__	)
															pair_keys.append(key.replace("""lora_up"""    ,				"""lora_down"""	)	)

										# update weight
										if len(state_dict[pair_keys[0]].shape	) == 4:
															lowercase_					=      state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
															lowercase_					=      state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
															curr_layer.weight.data += alpha * torch.mm(a__    ,				a__	).unsqueeze(2	).unsqueeze(3	)
										else:
															lowercase_					=      state_dict[pair_keys[0]].to(torch.floataa	)
															lowercase_					=      state_dict[pair_keys[1]].to(torch.floataa	)
															curr_layer.weight.data += alpha * torch.mm(a__    ,				a__	)

										# update visited list
										for item in pair_keys:
															visited.append(a__	)

					return pipeline


if __name__ == "__main__":
							a       	=  argparse.ArgumentParser()

							parser.add_argument(
							    '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
							)
							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument(
							    '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
							)
							parser.add_argument(
							    '--lora_prefix_text_encoder',
							    default='lora_te',
							    type=str,
							    help='The prefix of text encoder weight in safetensors',
							)
							parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
							parser.add_argument(
							    '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
							)
							parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

							a       	=  parser.parse_args()

							a       	=  args.base_model_path
							a       	=  args.checkpoint_path
							a       	=  args.dump_path
							a       	=  args.lora_prefix_unet
							a       	=  args.lora_prefix_text_encoder
							a       	=  args.alpha

							a       	=  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

							a       	=  pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




